Latest Activity

By Gunnar Carlsson December 3, 2018In their very provocative paper, Peter Battaglia and his colleagues, posit that in order for artificial intelligence (AI) to achieve the capabilities of human intelligence, it must be able to compute with highly structured data types, such as the ones humans deal with.…See More

By Gunnar Carlsson December 3, 2018In their very provocative paper, Peter Battaglia and his colleagues, posit that in order for artificial intelligence (AI) to achieve the capabilities of human intelligence, it must be able to compute with highly structured data types, such as the ones humans deal with.…See More

By Gunnar CarlssonThe appeal of forecasting the future is very easy to understand, even though it is not realizable. That has not stopped an entire generation of analytics companies from selling such a promise. It also explains the myriad methods that attempt to give partial, inexact, and probabilistic information about the future.Even if they could deliver on a crystal ball, such a capability would obviously have enormous…See More

Deep neural nets typically operate on “raw data” of some kind, such as images, text, time series, etc., without the benefit of “derived” features. The idea is that because of their flexibility, neural networks can learn the features relevant to the problem at hand, be it a classification problem or an estimation problem. Whether derived or learned, features are important. The challenge is in determining how one might use what one learned from the features in future work (staying inside the…See More

Deep neural nets typically operate on “raw data” of some kind, such as images, text, time series, etc., without the benefit of “derived” features. The idea is that because of their flexibility, neural networks can learn the features relevant to the problem at hand, be it a classification problem or an estimation problem. Whether derived or learned, features are important. The challenge is in determining how one might use what one learned from the features in future work (staying inside the…See More

In my earlier post I discussed how performing topological data analysis on the weights learned by convolutional neural nets (CNN’s) can give insight into what is being learned and how it is being learned. The significance of this work can be summarized as follows:It allows us to gain understanding about how the neural network performs a…See More

In my earlier post I discussed how performing topological data analysis on the weights learned by convolutional neural nets (CNN’s) can give insight into what is being learned and how it is being learned. The significance of this work can be summarized as follows:It allows us to gain understanding about how the neural network performs a…See More

TLDR: Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way. The implications of the finding are profound and can accelerate the development of a wide range of applications from self-driving everything to GDPR.IntroductionNeural networks have demonstrated a great deal of success in the study of various kinds of data, including images, text,…See More

TLDR: Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way. The implications of the finding are profound and can accelerate the development of a wide range of applications from self-driving everything to GDPR.IntroductionNeural networks have demonstrated a great deal of success in the study of various kinds of data, including images, text,…See More

The appeal of forecasting the future is very easy to understand, even though it is not realizable. That has not stopped an entire generation of analytics companies from selling such a promise. It also explains the myriad methods that attempt to give partial, inexact, and probabilistic information about the future.

Deep neural nets typically operate on “raw data” of some kind, such as images, text, time series, etc., without the benefit of “derived” features. The idea is that because of their flexibility, neural networks can learn the features relevant to the problem at hand, be it a classification problem or an estimation problem. Whether derived or learned, features are important. The challenge is in determining how one might use what one learned from the features in future work (staying…

In my earlierpostI discussed how performing topological data analysis on the weights learned by convolutional neural nets (CNN’s) can give insight into what is being learned and how it is being learned.